lightmotif 
lightmotif A lightweight platform-accelerated library for biological motif scanning using position weight matrices.
Motif scanning with position weight matrices (also known as position-specific scoring matrices) is a robust method for identifying motifs of fixed length inside a biological sequence. They can be used to identify transcription factor binding sites in DNA, or protease cleavage site in polypeptides. Position weight matrices are often viewed as sequence logos:
The lightmotif library provides a Rust crate to run very efficient
searches for a motif encoded in a position weight matrix. The position
scanning combines several techniques to allow high-throughput processing
of sequences:
permute instructions of AVX2.Other crates from the ecosystem provide additional features if needed:
lightmotif-tfmpvalue is an exact reimplementation of the TFMPvalue[4] algorithm for converting between a score and a P-value for a given scoring matrix.lightmotif-transfac is a parser for position-specific scoring matrices in the TRANSFAC format.This is the Rust version, there is a Python package available as well.
```rust use lightmotif::*; use lightmotif::abc::Nucleotide; use typenum::U32;
// Create a count matrix from an iterable of motif sequences
let counts = CountMatrix::
// Create a PSSM with 0.1 pseudocounts and uniform background frequencies. let pssm = counts.tofreq(0.1).toscoring(None);
/// Create a pipeline to run tasks with platform acceleration let pli = Pipeline::dispatch();
// Use the pipeline to encode the target sequence into a striped matrix let seq = "ATGTCCCAACAACGATACCCCGAGCCCATCGCCGTCATCGGCTCGGCATGCAGATTCCCAGGCG"; let encoded = pli.encode(seq).unwrap(); let mut striped = pli.stripe(encoded);
// Use the pipeline to compute scores for every position of the matrix. striped.configure(&pssm); let scores = pli.score(&striped, &pssm);
// Scores can be extracted into a Vec
// The highest scoring position can be searched with a pipeline as well. let best = pli.argmax(&scores).unwrap(); assert_eq!(best, 18);
``` This example uses a dynamic dispatch pipeline, which selects the best available backend (AVX2, SSE2, NEON, or a generic implementation) depending on the local platform.
Both benchmarks use the MX000001
motif from PRODORIC[5], and the
complete genome of an
Escherichia coli K12 strain.
Benchmarks were run on a i7-10710U CPU running @1.10GHz, compiled with --target-cpu=native.
Score every position of the genome with the motif weight matrix:
console
test bench_avx2 ... bench: 4,510,794 ns/iter (+/- 9,570) = 1029 MB/s
test bench_sse2 ... bench: 26,773,537 ns/iter (+/- 57,891) = 173 MB/s
test bench_generic ... bench: 317,731,004 ns/iter (+/- 2,567,370) = 14 MB/s
Find the highest-scoring position for a motif in a 10kb sequence
(compared to the PSSM algorithm implemented in
bio::pattern_matching::pssm):
console
test bench_avx2 ... bench: 12,797 ns/iter (+/- 380) = 781 MB/s
test bench_sse2 ... bench: 62,597 ns/iter (+/- 43) = 159 MB/s
test bench_generic ... bench: 671,900 ns/iter (+/- 1,150) = 14 MB/s
test bench_bio ... bench: 1,193,911 ns/iter (+/- 2,519) = 8 MB/s
Found a bug ? Have an enhancement request ? Head over to the GitHub issue tracker if you need to report or ask something. If you are filing in on a bug, please include as much information as you can about the issue, and try to recreate the same bug in a simple, easily reproducible situation.
This project adheres to Semantic Versioning and provides a changelog in the Keep a Changelog format.
This library is provided under the open-source MIT license.
This project was developed by Martin Larralde during his PhD project at the European Molecular Biology Laboratory in the Zeller team.